Integrating Preprocessing Methods and Convolutional Neural Networks for Effective Tumor Detection in Medical Imaging

TL;DR

This paper combines advanced preprocessing techniques with CNNs to improve tumor detection accuracy in medical imaging, demonstrating promising results for healthcare diagnostics.

Contribution

It introduces a novel integration of preprocessing methods with CNNs specifically tailored for tumor detection in medical images.

Findings

Preprocessing techniques significantly improve CNN detection accuracy.

The combined approach achieves high precision in tumor classification.

Data augmentation enhances model generalization.

Abstract

This research presents a machine-learning approach for tumor detection in medical images using convolutional neural networks (CNNs). The study focuses on preprocessing techniques to enhance image features relevant to tumor detection, followed by developing and training a CNN model for accurate classification. Various image processing techniques, including Gaussian smoothing, bilateral filtering, and K-means clustering, are employed to preprocess the input images and highlight tumor regions. The CNN model is trained and evaluated on a dataset of medical images, with augmentation and data generators utilized to enhance model generalization. Experimental results demonstrate the effectiveness of the proposed approach in accurately detecting tumors in medical images, paving the way for improved diagnostic tools in healthcare.